The data we have chosen for this project is the Australian Road Deaths Database (ARDD) provided by the Australian Bureau of Infrastructure and Transport Research Economics (BITRE). The records are updated monthly from 1986 to 2021. Here is the link to the database : https://data.gov.au/dataset/ds-dga-5b530fb8-526e-4fbf-b0f6-aa24e84e4277/details.
There are two data files. The fatality data contains the demographic and details of people who have died within 30 days of the traffic accident due to injuries caused by an Australia road crash. The fatal crashes data contains the records for the crash, including information like the road type, and speed limit. The two data files can be connected by the crash ID. See Appendix for more detailed descriptions of each variable.
Q1: What demographic has a higher rate of traffic accidents?
Q2: Which road user is more prone to accident with respect to States and over the Years?
Q3: How does the car accident fatality link with the district and accident type?
Q4: Is there any correlation between accidents and holiday? And why are some explanations for the relation(s) or lack thereof.
Q5 :Has accident rate decreased or increased within the past decade (2010- 2020)?
Q6: Possible correlations between number of fatalities and speed limit zone
## Crash.ID State Month Year Crash.Type Number.Fatalities Bus.Involvement
## 1 20202001 Vic 12 2020 Multiple 1 No
## 2 20204057 SA 12 2020 Single 1 No
## 3 20203145 Qld 12 2020 Single 1 No
## 4 20203208 Qld 12 2020 Single 1 No
## 5 20203117 Qld 12 2020 Multiple 2 No
## 6 20201199 NSW 12 2020 Single 1 No
## Heavy.Rigid.Truck.Involvement Articulated.Truck.Involvement Speed.Limit
## 1 No Yes 100
## 2 No No 100
## 3 No No 60
## 4 No No 100
## 5 No Yes 100
## 6 No No 50
## National.LGA.Name.2017 Christmas.Period Easter.Period Time.of.day
## 1 Horsham (RC) Yes No Night
## 2 Tatiara (DC) No No Day
## 3 Sunshine Coast (R) No No Night
## 4 Bundaberg (R) Yes No Night
## 5 Cloncurry (S) Yes No Day
## 6 Upper Lachlan No No Day
## Crash.ID State Month Year Crash.Type Bus.Involvement
## 1 20203116 Qld 12 2020 Multiple No
## 2 20201210 NSW 12 2020 Single No
## 3 20203202 Qld 12 2020 Single No
## 4 20201093 NSW 12 2020 Single No
## 5 20203161 Qld 12 2020 Multiple No
## 6 20203179 Qld 12 2020 Multiple No
## Heavy.Rigid.Truck.Involvement Articulated.Truck.Involvement Speed.Limit
## 1 No No 60
## 2 No No 100
## 3 No No 100
## 4 No No 100
## 5 No No 70
## 6 No No 100
## Road.User Gender Age National.LGA.Name.2017 Christmas.Period
## 1 Motorcycle rider Male 28 Cairns (R) No
## 2 Passenger Male 89 Walcha Yes
## 3 Driver Male 21 Charters Towers (R) Yes
## 4 Driver Male 81 Walcha No
## 5 Driver Female 66 Moreton Bay (R) No
## 6 Passenger Male 79 Lockyer Valley (R) No
## Easter.Period Age.Group Time.of.day
## 1 No 26_to_39 Night
## 2 No 75_or_older Day
## 3 No 17_to_25 Day
## 4 No 75_or_older Night
## 5 No 65_to_74 Night
## 6 No 75_or_older Day
Figure 3.1: Australian road deaths database and fatal crashes, by age and gender
Figure 3.2: Australian road deaths database and fatal crashes, by age and gender
we group the data by states and the type of Road Users which have involved in the fatal accidents.
Figure 3.3: Number of Fatal Accidents with Different Road User in Different State
From the graph above 3.3:
In all the states
Interpretation:
First of all, we group the data by state and the type of car which have involved in the fatal accidents.
## # A tibble: 6 x 2
## # Groups: State [6]
## State Bus_Involved
## <chr> <int>
## 1 ACT 4
## 2 NSW 75
## 3 NT 6
## 4 Qld 43
## 5 SA 13
## 6 Tas 7
## # A tibble: 6 x 2
## # Groups: State [6]
## State Heavytruck_Involved
## <chr> <int>
## 1 ACT 3
## 2 NSW 266
## 3 NT 5
## 4 Qld 144
## 5 SA 53
## 6 Tas 27
## # A tibble: 6 x 2
## # Groups: State [6]
## State Articulated_truck_Involved
## <chr> <int>
## 1 ACT 6
## 2 NSW 344
## 3 NT 15
## 4 Qld 278
## 5 SA 104
## 6 Tas 29
## # A tibble: 6 x 3
## # Groups: State [2]
## State Type Count
## <chr> <chr> <int>
## 1 ACT Bus_Involved 4
## 2 ACT Heavytruck_Involved 3
## 3 ACT Articulated_truck_Involved 6
## 4 NSW Bus_Involved 75
## 5 NSW Heavytruck_Involved 266
## 6 NSW Articulated_truck_Involved 344
In this table, we have got the count of different car type of fatal accidents happened group by different states. This data is in long format which will be easier for us to plot the bar chart for better visualization.
From the graph above:
In all the states:
Interpretation:
Articulated truck is the heaviest among these 3 type of cars. Heavier car impose a greater impact on any accident which cause a higher fatalities.
Articulated truck is a vehicle which has a permanent or semi-permanent pivot joint in its construction. Only large vehicles need this structure which is easier for them to turn. But it also impose an additional risk for higher fatalities in an accident due to the complex mechanical structure.
Then, we group the data by the crash type and count the number of it.
## # A tibble: 6 x 3
## # Groups: State, Crash.Type [6]
## State Crash.Type Count
## <chr> <chr> <int>
## 1 ACT Multiple 57
## 2 ACT Single 49
## 3 NSW Multiple 1783
## 4 NSW Single 2112
## 5 NT Multiple 129
## 6 NT Single 333
From the graph above:
In all the states except ACT and TAS:
How does the car accident fatality link with the district?
## # A tibble: 6 x 3
## # Groups: State, National.LGA.Name.2017 [6]
## State National.LGA.Name.2017 n
## <chr> <chr> <int>
## 1 Qld <NA> 1786
## 2 NSW <NA> 1778
## 3 Vic <NA> 1638
## 4 WA <NA> 954
## 5 SA <NA> 415
## 6 NT <NA> 220
## # A tibble: 6 x 3
## # Groups: State, National.LGA.Name.2017 [6]
## State National.LGA.Name.2017 n
## <chr> <chr> <int>
## 1 Qld Brisbane (C) 87
## 2 NSW Central Coast 82
## 3 Qld Gold Coast (C) 68
## 4 Qld Moreton Bay (R) 62
## 5 NSW Lake Macquarie 56
## 6 NSW Shoalhaven 55
From the table above:
Interpretation:
## [1] 12 4 1 3
Figure 3.4: The fatal crashes happen in Christmas period from 2018 to 2020
It can be seen from 3.4 that :
The number of crashes increases in VIC, WA and NSW in 2018 and 2019 respectively during Christmas period.
The OLD got a sudden decline in Christmas period of 2020, maybe due to the effectof COVID virus.
For other states, the crashes either fluctuated or drop. Therefore, there is no dominate correlation between crashes and Christmas.
Figure 3.5: The crashes in Easter period from 2018 to 2020
In Figure 3.5, in 2018, only WA has a slightly increase in Easter period. For Victoria, the number of crashes drops dramatically in March to April of 2018 and 2020. While other states fluctuate in these period. There is no clear correlation between cashes in Easter period in different states.
Figure 3.6: Fatal crashes happens by day and night
The Figure 3.6 compares the fatal crashes that happen in days and night. + Obviously, there are more crashes happen on the days than night.
In 2018 and 2013, the crashes of ACT happen more on night rather than day.
In NT, some crashes happens more at night than day in 2020, 2018, 2015.
Although 2019 was the year the pandemic started, it surprisingly was not the year with the lowest number of fatal crashes in total.
| Year | <= 40 | 50 | 60 | 70-90 | >= 100 |
|---|---|---|---|---|---|
| 2010 | 7 | 116 | 236 | 283 | 588 |
| 2011 | 18 | 147 | 181 | 239 | 554 |
| 2012 | 21 | 132 | 248 | 277 | 499 |
| 2013 | 16 | 131 | 202 | 251 | 492 |
| 2014 | 18 | 111 | 192 | 229 | 490 |
| 2015 | 20 | 113 | 222 | 249 | 487 |
| 2016 | 17 | 132 | 215 | 290 | 536 |
| 2017 | 35 | 152 | 202 | 241 | 487 |
| 2018 | 18 | 131 | 192 | 228 | 477 |
| 2019 | 21 | 129 | 185 | 241 | 515 |
| 2020 | 18 | 126 | 171 | 234 | 452 |
The speed limits are split into 5 respective parts which is 40 and below, 50, 60, 70-90 and 100 and above, all measured in kilometer per hour (km/h). Areas which have less than 40 km/h are often shared zones, school zones and places with high density of pedestrian. 50 km/h are default speed limit within built up areas in every state in Australia except for Northern Territory. 60 km/h are sub-arterial roads, as well as the default speed within built up area in Northern Territory. 70-90 are connector and small highways. 100 and above area highways speed limits.
Between 2010 and 2020, table 3.1 showed that the overall trend in fatal crashes is reduced for speed limit zones outside built-up areas but increased for zones within built-up areas. However, within the period of 2015-2016, all zones see an increase in number of fatal crashes. The number of fatal crashes reduced or increased in varying number as well as rate across the speed zones. With the speed zones of 100 and above and 60 km/h decreased by largest proportion, about 25%.
Figure 3.7: Comparing proportion of yearly fatal crash by speed limit
One notable point as figure 3.7 showed is throughout the period analyzed, not only is the speed zone of 100 km/h and above has highest percentage of fatal crash, but it accounts for nearly 50% of all fatal crashes in Australia. While 50 km/h and below speed zones account for less than 10% of all fatal crashes. It is generally well known that the higher the speed a vehicle is traveling at, the longer the stopping distance is, and thus fatal crash is more likely to occur on highways. Kloeden, Woolley & McLean (2007) found that following the reduction of urban speed limit from 60 to 50 km/h in South Australia in 2003, there was a 23% reduction in fatal crashes in 50 km/h zones and 16% reduction in 60 km/h zones.
Figure 3.8: Comparing number of fatalities and fatal crashes by speed zone
Figure 3.9: Percentage difference between fatalities and fatal crashes
Figure 3.8 illustrated that not only the number of fatal crashes increased with speed limits, but number of fatalities increased as well, and in higher proportion than the increase of crashes. The difference between number of fatalities and number of crashes are made clearer in figure 3.9. The percentage difference is similar in speed zones of 60 km/h and below, at around 3%. However, the difference for 70-90 km/h is approx. 8% and for highway speed is nearly 13%.
Kloeden, C., Woolley, J., & McLean, A. J. (2007, October). A follow-up evaluation of the 50km/h default urban speed limit in South Australia. In Proceedings of.